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# general visualisation
library('ggplot2') # visualisation
library('scales') # visualisation
library('patchwork') # visualisation
library('RColorBrewer') # visualisation
library('corrplot') # visualisation## corrplot 0.84 loaded# general data manipulation
library('dplyr') # data manipulation##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary('readr') # input/output##
## Attaching package: 'readr'## The following object is masked from 'package:scales':
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## col_factorlibrary('vroom') # input/output
library('skimr') # overview
library('tibble') # data wrangling
library('tidyr') # data wrangling
library('purrr') # data wrangling##
## Attaching package: 'purrr'## The following object is masked from 'package:scales':
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## discardlibrary('stringr') # string manipulation
library('forcats') # factor manipulation
# specific visualisation
library('alluvial') # visualisation
library('ggrepel') # visualisation
library('ggforce') # visualisation
library('ggridges') # visualisation
library('gganimate') # animations## No renderer backend detected. gganimate will default to writing frames to separate files
## Consider installing:
## - the `gifski` package for gif output
## - the `av` package for video output
## and restarting the R sessionlibrary('GGally') # visualisation## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2##
## Attaching package: 'GGally'## The following object is masked from 'package:dplyr':
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## nasalibrary('ggthemes') # visualisation
library('wesanderson') # visualisation
library('kableExtra') # display##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
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## group_rows# Date + forecast
library('lubridate') # date and time##
## Attaching package: 'lubridate'## The following objects are masked from 'package:dplyr':
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## intersect, setdiff, union## The following objects are masked from 'package:base':
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## date, intersect, setdiff, unionlibrary('forecast') # time series analysis## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo#library('prophet') # time series analysis
library('timetk') # time series analysis
# Interactivity
library('crosstalk')
library('plotly')##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layout# parallel
library('foreach')##
## Attaching package: 'foreach'## The following objects are masked from 'package:purrr':
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## accumulate, whenlibrary('doParallel')## Loading required package: iterators## Loading required package: parallellibrary(vroom)
library(stringr)
library(tidyverse)
train <- vroom(str_c('/Coursework/Timeseries/Timeseries_project/CSV files/sales_train_validation.csv'), delim = ",", col_types = cols())
prices <- vroom(str_c('/Coursework/Timeseries/Timeseries_project/CSV files/sell_prices.csv'), delim = ",", col_types = cols())
calendar <- read_csv(str_c('/Coursework/Timeseries/Timeseries_project/CSV files/calendar.csv'), col_types = cols())
sample_submit <- vroom(str_c('/Coursework/Timeseries/Timeseries_project/CSV files/sample_submission.csv'), delim = ",", col_types = cols())extract_ts <- function(df){
min_date <- as.Date("2011-01-29")
df %>%
select(id, starts_with("d_")) %>%
pivot_longer(starts_with("d_"), names_to = "dates", values_to = "sales") %>%
mutate(dates = as.integer(str_remove(dates, "d_"))) %>%
mutate(dates = min_date + dates - 1) %>%
mutate(id = str_remove(id, "_validation"))
}
set.seed(4321)
foo <- train %>%
sample_n(50)
ts_out <- extract_ts(foo)
cols <- ts_out %>%
distinct(id) %>%
mutate(cols = rep_len(brewer.pal(7, "Set2"), length.out = n_distinct(ts_out$id)))
ts_out <- ts_out %>%
left_join(cols, by = "id")
pal <- cols$cols %>%
setNames(cols$id)shared_ts <- highlight_key(ts_out)
palette(brewer.pal(9, "Set3"))
gg <- shared_ts %>%
ggplot(aes(dates, sales, col = id, group = id)) +
geom_line() +
scale_color_manual(values = pal) +
labs(x = "Date", y = "Sales") +
theme_tufte() +
NULL
filter <- bscols(
filter_select("ids", "Sales over time: Select a time series ID (remove with backspace key, navigate with arrow keys):", shared_ts, ~id, multiple = TRUE),
ggplotly(gg, dynamicTicks = TRUE),
widths = c(12, 12)
)## Warning in bscols(filter_select("ids", "Sales over time: Select a time series
## ID (remove with backspace key, navigate with arrow keys):", : Sum of bscol width
## units is greater than 12bscols(filter)